Vehicle speed-based analytics

ABSTRACT

A system for optimizing a travel speed of an off-road vehicle utilizes time-over-target values as a threshold indicator for diagnostics and subsequent remedial actions. A road used for haulage is divided into a plurality of predetermined road segments. A target transit time is defined for each of the predetermined segments to provide a target speed curve. An actual transit time values is measured for the off-road vehicle in transiting the road. A comparison outcome is generated by comparing the target transit time value with the actual transit time value. The comparison outcome is useful in adjusting the travel speed for the vehicle to minimize the time over target value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 15/376,030, filed on Dec. 12, 2016, now allowed, which claimsthe benefit of U.S. provisional patent application No. 62/267,486, filedon Dec. 15, 2015, both of which are hereby incorporated by reference forall that they disclose.

BACKGROUND 1. Field

The presently disclosed instrumentalities relate to systems and methodsof managing vehicle travel in general and, more particularly, to systemsand methods of optimizing the travel speed of off-road haul trucks.

2. Description of the Related Art

Open pit mines utilize fleets of specialized vehicles that are speciallyadapted for heavy haul utilization. These vehicles include, for example,the Model 793F, 797F and MT4400D AC vehicles manufactured by Caterpillarof Peoria, Ill., which have nominal payload capacities ranging from 221to 363 metric tonnes. These vehicles may be purchased on commercialorder equipped with controller area network (CAN) systems.

Data from heavy haul vehicles may be broadcast for use at a monitoringstation. This is shown, for example, in U.S. Pat. No. 7,987,027, whichshows use of CAN technology on mining vehicles. A wireless transmissionsystem provides for data download/upload functionality to an off-boardmonitoring system. The wireless transmission system preprocessesacquired machine data and combines data sets to reduce bandwidth inaccommodating low-frequency, low bandwidth networks of a type commonlyused in mines.

CAN systems such as these provide an overwhelming amount of dataconcerning the status of various vehicle components. System readings mayinclude, without limitation, what gear the vehicle is operating in at aparticular time; compositional analysis of engine exhaust contents suchas soot, fuel vapor, carbon monoxide and the like; pressuredifferentials across such engine components as filters, blowers and thelike; tire pressure; alternator output; battery voltage; temperaturesincluding coolant temperature, oil temperature, cab temperature, braketemperature, external temperature and the like; intervals of brakeactivation; intervals of acceleration and deceleration; windshield wiperactivation; grade of road; steering patterns indicative of operatorfatigue; hydraulic pump output pressure; quantity of fuel on board, andsuspension strut gas pressure. Utilization of this data is typicallydirected towards analytics for maintenance needs or monitoring ofindividual vehicles to assure operations within parameters as requiredunder vehicle warranty. Generally speaking, the analytics have notprogressed beyond these factors to facilitate improved fleet operations.

SUMMARY

The presently disclosed instrumentalities overcome the problems outlinedabove and advance the art by providing transport vehicle diagnosticsthat may be utilized to improve vehicle fleet operations. In particular,the vehicle diagnostics include a comparison outcome based upon the timethat is required for a vehicle to accomplish a particular task versus atarget time that should be achievable by the vehicle in accomplishingthe task.

According to one embodiment, a method of optimizing a travel speed for avehicle at a remote location includes dividing a road to be traversed bythe vehicle into a plurality of predefined road segments. A targettransit time value is determined for the vehicle in transiting each ofthe plurality of predefined road segments. An actual transit time valueis ascertained for the vehicle as the vehicle travels over at least oneof the predefined road segments. A comparison is made between the targettransit time value and the actual transit time value for the at leastone predetermined road segment to provide a comparison outcome. Thecomparison outcome is utilized an indicator to provide a resolution orrecommendation affecting at least one of road quality at the remotelocation, vehicle maintenance, vehicle operator training and operationalscheduling at the remote location. The remote location may be, forexample, an open-pit mine.

According to one embodiment, the foregoing method is implemented in asystem for optimizing a travel speed for an off-road vehicle. The systemincludes a telecommunications network and a vehicle. The vehicleincludes a vehicle network having one or more sensors operativelyassociated with the vehicle for providing data selected from the groupconsisting of at least one of time and a speed of the vehicle. Thevehicle also includes a transmitter configured to upload the data to thetelecommunications network. A processing system is operativelyassociated with the telecommunications network to operate on the data.The processing system is configured with program logic to implement theforegoing method, which may result in maintenance operations toimplement the resolution or recommendation according to the programlogic. This implementation may include, for example, automatedscheduling of maintenance operations according to a system of expertrules.

In one aspect, a conventional heavy haul truck may be improved byproviding access a telecommunications network for the transfer of datato a system providing an analytical capability to implement theforegoing method. A database is configured to operate on data that isuploaded from the truck where the data is associated with a plurality ofpredefined road segments dividing a road to be traversed by a vehicle. Acomputer equipped with program logic determines a target transit timevalue for the vehicle in transiting each of the plurality of predefinedroad segments. This may be done, for example, using a lookup table or acorrelation. The computer accesses the data to ascertain an actualtransit time value for the vehicle as the vehicle travels over at leastone of the predefined road segments. The computer compares the targettransit time value and the actual transit time value for the at leastone predetermined road segment to provide a comparison outcome. Thecomputer optionally but preferably utilizes the comparison outcome as anindicator to provide a resolution or recommendation affecting at leastone of road quality at the remote location, vehicle maintenance, vehicleoperator training and operational scheduling at the remote location.

In one aspect, the program logic may be provided on a non-transitorycomputer-readable storage medium having computer-executable instructionsembodied thereon that, when executed by at least one computer processorcause the processor to perform the foregoing method. The non-transitorycomputer-readable storage medium may be, for example, a computer harddrive, a flash memory stick, or a CD-ROM.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative and presently preferred exemplary embodiments of theinvention are shown in the drawings in which:

FIG. 1 is a schematic representation of one embodiment of a system forassessing TOT_(i) values for vehicles according to the presentlydisclosed instrumentalities;

FIG. 2 shows a CAN network that may be used to equip one or more of thevehicles of FIG. 1;

FIG. 3 is a flow chart of one embodiment of a method of utilizingTOT_(i) comparison outcomes as a threshold indicator of vehicleoperations and diagnostics where the method of FIG. 3 may be implementedas software programming a computer with machine instructions for theperformance of this method;

FIG. 4 shows a road that has been divided into predetermined segmentsfor use in the method of FIG. 3;

FIG. 5 is a process diagram that provides additional detail about theperformance of a method step from FIG. 3 according to one embodiment;

FIG. 6 is a heat map that may be generated by use of a comparisonoutcome produced according to the method of FIG. 3 showing classifiedTOT_(i) values according to a presentation scheme that may be hashed orcolorized to indicate a relative scale of vehicle performance on thepredetermined segments of the road shown in FIG. 4;

FIG. 7 is an exemplary plot of a target speed curve for an unloadedoff-road vehicle illustrating various target off-road vehicle speeds forvarious road grades;

FIG. 8 is an exemplary plot of a target speed curve for a fully loadedoff-road vehicle illustrating various target off-road vehicle speeds forvarious road grades; and

FIG. 9 shows post-processing that may occur downstream of a TOT_(i)comparison outcome for purposes of improving operation efficiency of anindustrial operation where haulage is involved.

DETAILED DESCRIPTION

FIG. 1 shows one embodiment of a system 100 used to optimize the travelspeeds of one or more vehicles, such as haul trucks 102, 104, 106, 108,110. Each of the haul trucks is equipped with a two-way wirelesscommunications linkage, such as linkages 112, 114 respectivelyincorporated in haul tucks 108, 110. The linkages 112, 114 are inwireless communication with one or more wireless access points 116, suchas a radio tower or a Bluetooth Scatternet, which is constructed andarranged to provide wireless communications from a remote location suchas an open pit mine or lumbering operation. The wireless access point116 communicates data to and from a server/router combination 118/120.The router 120 processes packetized communications from the remotelocation through satellite dish 122, utilizing communications linkage124 to communicate with a satellite network 126. The satellite network126 communicates through commercial linkages 128 established by one ormore commercial service providers to gain access to the Internet 130 forcommunication with router/server combination 132/134. Data from server134 is accordingly provided to computer 136 and stored on database 140.It will be appreciated that the computer 136 with associated display 138and database 140 may be located at a central location, but also that theassociated computing and data storage functionalities described belowmay be distributed, as in the case of distributed databasing and/ormassively parallel computing. Moreover, the network design may vary bymethods known to the art to accommodate an infinite number of hardwarechoices based upon the need of any particular location. For example, thesatellite 126 is unnecessary if there is a different telecommunicationsnetwork available, or if the central computer 136 is located at a remotelocation such as a mine. There may be any number of haul trucks 102-110,and the computer 136 may be used to monitor more than one remotelocation.

The haul trucks 102-110 form a fleet of trucks in used at a particularlocation, such as a mine or lumbering operation. Each of these trucksare equipped with a vehicle network (not shown). The vehicle networksprovide data sense and reporting functionalities that facilitatemonitoring of vehicle components. Commercially available vehiclenetworks include, for example, Local Interconnect Networks (“LIN;” seeISO 9141 and ISO 17987) suited for low date rate applications,Controller Area Networks (“CAN;” see ISO11898) for medium data rateapplications; and FlexRay (ISO 17458) for safety critical applications.A haul truck may contain more than one vehicle network.

The vehicle networks are frequently CANs. CAN is a multi-master serialbus standard for connecting Electronic Control Units, which function asnodes on the CAN. Two or more nodes are required on the CAN network tocommunicate. The node may be a simple I/O device or an embedded computerwith a CAN interface and sophisticated software. The node may also be agateway allowing a standard computer to communicate over a USB orEthernet port to the devices on a CAN network. CANs have been used tomonitor sensors in a variety of application including, withoutlimitation, brake sensors, wheel sensors, pitch/roll/yaw sensors, fluidlevel sensors (fuel, oil, hydraulic fluid, etc.), hydraulic cylinderposition sensors, truck bed position sensors, bucket/blade/implementposition sensors, tire health sensors (pressure, temperature, tread,etc.), exhaust sensors (temperature, NOx, etc.), engine sensors (enginespeed, engine load, fuel pressure, boost pressure, etc.), transmissionsensors (gear, input/output speed, slip times, etc.), torque convertersensors (input speed, output speed, temperature, etc.), various othermachine parameter sensors (payload, strut pressure, machine speed,etc.), and various operator cabin sensors (vibration, ignition keypresence/position, seat position, seat belt position, door position, andsettings/positions of operator controls, etc.).

FIG. 2 shows a network schematic that represents individual vehiclenetworks found on each of the haul trucks 102-110. As illustrated, thenetwork 200 is a controller area network (“CAN”), but in alternativeembodiments may also be a LIN, MOST, FlexRay or other type of vehiclenetwork.

Network 200 is a multi-master network utilizing the CAN multi-masterarchitecture as is standardized in the art. Each node, such as nodes202, 204, 206, 208 210, 212, includes a node controller and atransceiver configured to receive and transmit data on a CAN Bus-lineincluding CANB component 214 and CANH component 216. The components 214,216 are useful in arbitrating to resolve or arbitrate data low priorityversus high priority data transmission conflicts as is known to the art.Each node of nodes 202-212 is configured to provide a particularfunctionality. Thus, node 202 provides sensor output indicative ofvehicle speed. This may be done, for example, by measuring revolutionsper minute (rpm) as the rotational speed of a wheel or transaxle using amagnetic pickoff that counts the incidents of magnetic field proximityvariations proximate a sensor over an interval of time as an indicatorof vehicle speed. As an alternative way to assess vehicle speed, node202 may provide output indicating vehicle speed as represented on anoperator's dashboard display, where the vehicle speed may be determinedby any system known to the art. Node 204 is a transceiver configured totransmit and receive data on system 100. Node 206 may provide outputfrom a Global Positioning System (GPS) to associate vehicle locationwith a particular time. Node 208 includes a digital clock or timingcircuit that may provide a time stamp for any data transmission onnetwork 200. Node 210 provides output indicating that a vehicle-mountedRadio Frequency IDentification (RFID) tag or other proximity detectionsystem has been activated by proximity to short range or near-fieldcircuitry dedicated to that purpose. A processing node 212 may filter,delimit, screen or operate on data transmitted for the purposesdescribed herein.

As will be appreciated by those of ordinary skill in the art, thenetwork 200 is not strictly limited to the nodes shown in FIG. 2, nor isit necessary that the network 200 have all of the nodes shown in FIG. 2.By way of example, network 200 may also comprise one or moreenvironmental sensors (not shown), for example, including light sensors,rain sensors, fog sensors, and night sensors as described in EuropeanPatent Publication EP19980956367 to Schofield et al., These sensors maybe utilized as CAN nodes for sensing certain environmental conditions,such as the presence of rain, snow, or fog, at various locations withinthe environment. All types of data on the network 200 may be transmittedon system 100 (see FIG. 1). The processing node 212 may also operativelyconnect to one or more display systems (not shown) to display certaininformation and data to a driver. It is possible to utilize CAN-basedtechnology to monitor virtually every operational aspect of a vehicle.

FIG. 3 shows program logic implementing a method 300 of calculatingTOT_(i) values for use as described herein. The program logic may beimplemented, for example, on computer 136 of system 100 or theprocessing node 212 of network 200. Step 302 entails dividing a roadinto one or more predetermined segments. This is shown, by way ofexample, in context of a remote location 400 including mine road 402, asshown in FIG. 4. Road 402 extends between a dump site at location C₁ andproceeds between sequentially numbered segments C₂, C₃, C₄, etc. . . .to a loading point at location C₃₄. Accordingly, road 402 proceedsbetween positionally sequential segments C₁ to C₂, C₂ to C₃, C₃ to C₄,etc. . . . . It will be appreciated that these segments may be combinedfor analytical purposes, such as by defining a segment C₁ to C₄ thatincludes all of segments C₁ to C₂, C₂ to C₃, C₃ to C₄.

Each segment may be selected based upon a commonality of factorsaffecting vehicle speed. These factors include, for example, one or morefactors selected from the group including: (1) grade, (2) width of road,(3) curvature of road, (4) quality of road surface, (5) multiple vehicletransit rate, (6) environmental conditions, (7) payload of truck, (8)operator input, and (9) historical experience indicating differences inactual vehicle transit speeds. There may be any number of calloutsC_(i), where also a particular remote location 400 may have manydifferent roads and some of these roads may share segments. While in thecase of shared road segments it is not strictly necessary that eachsegment have a unique identifier in the system, it is preferred thateach segment have a unique identifier because this practice permitsuniform comparisons to be made for haulage over the same predeterminedsection of road.

Alternatively, the callouts C₁ to C₃₄ need not be associated with anyparticular feature or condition of the road and, instead, may beassigned at periodic callouts that are determined for example, atequidistant spacing. Generally speaking, increasing the number ofcallouts improves the analysis because shorter road segments C_(i) willpermit more resolution or “granularity” of the TOT_(i) valuesestablished for the road 402.

Transceivers 404, 406, 408, 410, 412, 414, 416, 418, 420 form part of anoptional pseudo-satellite system (the transceivers being referred to aspseudo-lites) that provides positional tracking of vehicles at theremote location 400. The pseudo-lite system may be, for example, asystem as described in U.S. Pat. No. 6,031,487 issued to Mickelson. Inaddition, each of the segment callouts C₁ to C₃₄ on road 402 may beoptionally equipped with RFID proximity detectors that emit a signalthat is detectable by RFID node 210 to confirm the physical presence ofa particular haul truck at the segment callout.

Once the road segments are determined in step 302, it is possible todefine a target speed curve 304 that provides target speeds for each ofthe road segments defined in step 302. Where the distance of each roadsegment is also known, it is possible to calculate 306 a target transittime according to Equation (1):

t _(i) =D _(i) /S _(i), where  (1)

-   -   i is an integer or other value indicating a predetermined road        segment, such as a segment of road 402, t_(i) is a target        transit time for the predetermined road segment, D_(i) is a        distance associated with the predetermined road segment, and        S_(i) is a target speed or velocity for the predetermined road        segment contributing to the overall target speed curve for road        402 as defined in step 304. It will be appreciated that the        values t_(i) and S_(i) may be utilized as target time values as        described below.

The transit time so determined from the target speed curve provides auniform basis for comparison; however, the comparison may alternativelybe based upon vehicle velocity acceleration, momentum or kinetic energy.The simplest case for a target speed curve is to utilize amanufacturer's recommendations for what operational speeds a vehicle iscapable of reaching under environmental conditions of load and grade.Practical experience has shown, however, that these recommendations areusually optimistic, except in the case of travelling empty on a downhillgrade. Moreover, the speeds may vary considerably by make and model ofvehicle.

It is possible to improve the comparison results by providing anexperiential model that is specific to vehicle type considering, forexample, actual driving results and/or input from expert vehicleoperators. The model ideally provides an achievable target speed foreach road segment as a function of any combination of input parametersincluding, for example, (1) grade, (2) width of road, (3) curvature ofroad, (4) quality of road surface, (5) whether slow and fast vehiclesco-exist on the road segment, (6) environmental conditions, and (7)existing payload of truck. The model may include operator input as asystem of expert rules. By way of example, a target speed curve may becalculated according for a Caterpillar 793B and 793D trucks using amodel according to a lookup table, which may be for example a lookuptable based upon actual experience in a working mine as shown Table 1.

TABLE 1 Haul Truck Target Speed Parameters Speed Make Model Status Grade(mph) Caterpillar 793B Loaded Uphill Speed 7 Caterpillar 793B LoadedDownhill Speed 10 Caterpillar 793B Loaded Flat Speed 26 Caterpillar 793BEmpty Uphill Speed 15 Caterpillar 793B Empty Downhill Speed 23Caterpillar 793B Empty Flat Speed 30 Caterpillar 793D Loaded UphillSpeed 7.8 Caterpillar 793D Loaded Downhill Speed 10 Caterpillar 793DLoaded Flat Speed 26 Caterpillar 793D Empty Uphill Speed 15 Caterpillar793D Empty Downhill Speed 23 Caterpillar 793D Empty Flat Speed 30

While the target speeds represented in Table 1 summarize the generalgrades of road as being either uphill, downhill or flat, it will beappreciated that a particular make and model of truck may have a speedthat varies more granularly as a function of altitude and angle ofgrade. Due to gearing and turbocharger considerations, the targetvelocity curve from, for example, a grade of −12 degrees to −5 to 0 to 5may be curved or nonlinear. Curves such as these may be correlated fromactual vehicle speeds based upon experience with a particular haulageoperation. The discussion of FIGS. 7 and 8 below provides an optionalbut preferable embodiment showing, by way of example, how to calculatethe target speed curve from an experientially based correlation insteadof a lookup table.

A haul truck transits the road 402 while the network 200 measures 308the actual vehicle speed and/or transit times over each of thepredetermined segments of road 402. FIG. 5 provides additional detail onthe performance of step 308 according to one embodiment that prioritizesthe calculation of transit time value according to different navigationoptions. FIG. 5 shows a form of process 308 that may be implemented on acomputer using program logic or software. By way of example, thesoftware may run as executable code on computer 136 of system 100 or onprocessing node 212 of network 200.

It will be appreciated that GPS data is relatively accurate if it can beacquired; however, certain operating environments, such as pit mines orlocations in forests or proximate mountainsides, interfere with GPSsatellite signals that are primarily line-of-sight. Accordingly, it ispossible to provide a plurality of alternative navigation options and toprioritize which one of these will provide the dominant form ofnavigation for calculation of transit time values when a haul truck istraversing road 402.

At the commencement of process step 308 as represented in FIG. 5, thehaul truck is located at a callout on road 402, such as callout C₁. Acounter is incremented 500 to indicate the next callout in positionalsequence and provide a distance between the present location and thenext callout. If a useable GPS signal is available 502 then the processutilizes that signal to monitor 510 and ascertain position of the haultruck as the haul truck arrives at the physical location of the nextcallout that was identified in step 500. If a usable GPS signal is notpresent, processing advances to step 504 which inquires whether a usablepseudo-light signal is available. If a usable pseudo-light signal isavailable, then the process utilizes the pseudo-light signal to monitor510 and ascertain position of the haul truck as the haul truck arrivesat the physical location of the next callout that was identified in step500. If a usable pseudo-light signal is unavailable then processingadvances to step 506, which inquires whether the next callout on road402 is equipped with RFID functionality. If the callout is so equippedwith RFID, then the process utilizes the RFID signal to monitor 510 andascertain the position of the haul truck as the haul truck arrives atthe physical location of the next callout that was identified in step500. If the next callout is not equipped with RFID functionality,processing advances to step 508 which utilizes data from speed node 202and clock node 208 to calculate a total distance traveled.Alternatively, the distance traveled may be determined from an odometerreading. When this distance equals the known distance to the nextcallout indicated by step 500, processing in step 510 ascertains thatthe haul truck is located at that next callout.

The clock of node 208 may polled to determine 512 a transit time betweenthe successive callouts on road 402 for a predetermined segment of road.Alternatively, the distance associated with the road segment, such asroad segment C₁ to C₂, may be divided by the average speed over thetransit time to ascertain an average transit time. The average speed maybe ascertained, for example, by accumulating the sensed speed atintervals of one second and dividing by the number of seconds.

If the haul truck has not reached the end of the road 402, as determinedat step 514, then processing proceeds to step 500 to again increment thecounter indicating the next segment of road and the process describedabove repeats itself for the next road segment, such as road segment C₂to C₃ following segment C₁ to C₂. If the haul truck has 504 reached theend of road 402, then the process broadcasts 516 the transit time valuedata or, alternatively, any data needed to calculate the transit timevalue. The counter sequence is then inverted 518, for example, bymultiplying the counter array i by −1, and the counter may beincremented as before for the return trip along road 402. In this waythe process provides transit time values for each segment of road 402,such as segment C₁ to C₂.

Returning now to FIG. 3, processing step 308 entails determining theTOT_(i) value for each of the predetermined road segments. This may bedone, for example, according to Equations (2) through (10) below, whichrepresent different options that may be used for calculating the valueotherwise represented herein as TOT_(i):

Comparison outcomes based upon actual time

TOT_(ti)=AT_(i)−TT_(i), where  (2)

-   -   i is an integer that identifies a particular road segment,        TOT_(ti) is time-over-target value for the road segment i,        AT_(i) is actual transit time for the road segment i (see e.g.,        step 512 of FIG. 5); and TT_(i) is a target time value for the        road segment i.

TOT_(ti)=(AT_(i)−TT_(i))/TT_(i)  (3)

TOT_(ti)=AT_(i)/TT_(i)  (4)

Comparison outcomes based upon velocity

TOT_(vi)=AV_(i)−TV_(i), where  (5)

-   -   TOT_(vi) is a velocity-based time-over-target value for the road        segment i, AV_(i) is actual average velocity for the road        segment i (see e.g., step 512 of FIG. 5); and TV_(i) is a target        average velocity for the road segment i.

TOT_(vi)=(AV_(i)−TV_(i))/TT_(i)  (6)

TOT_(vi)=AV_(i)/TV_(i)  (7)

Comparison outcomes based upon acceleration between road segments

TOT_(ai)=[(AV_(i)−TV_(i))/t _(i)−(AV₈₋₁−TV_(i-1))/t _(i-1)] where  (11)

-   -   TOT_(ai) is an acceleration-based time-over-target value for the        current road segment i, AV_(i) is actual average velocity for        the current road segment i (see e.g., step 512 of FIG. 5);        TV_(i) is a target average velocity for the current road segment        i, t_(i) is the actual transit time for the prior road segment        i, AV_(i-1) is actual average velocity for the prior road        segment i (see e.g., step 512 of FIG. 5); TV_(i-1) is a target        average velocity for the prior road segment i−1.    -   Comparison outcomes based upon momentum

TOT_(Mi)=AM_(i)−TM_(i), where  (8)

-   -   TOT_(Mi) is a momentum-based time-over-target value for the road        segment i, AM_(i) is actual vehicle momentum according to        average velocity for the road segment i (see e.g., step 512 of        FIG. 5); and TM_(i) is a target vehicle momentum according to a        target average velocity for the road segment i.

TOT_(Mi)=(AM_(i)−TM_(i))/TT_(i)  (9)

TOT_(Mi)=AM_(i)/TM_(i)  (10)

Comparison outcomes based upon kinetic energy

TOT_(Ki)=(mAV_(i) ² −mTV_(i) ²)/2 where  (8)

-   -   TOT_(Ki) is a kinetic energy-based time-over-target value for        the road segment i, AV_(i) is an average vehicle velocity for        the road segment i (see e.g., step 512 of FIG. 5); TV_(i) is a        target vehicle average velocity for the road segment I, and m is        mass of the vehicle including payload of the vehicle.

The foregoing examples teach by way of example and not by limitation. Itwill be appreciated that a variety of comparisons may be provided asdifferences or ratios relating TOT_(i) values to target transit timevalues.

Once the TOT_(i) values are complete, they may be presented to a systemuser using, for example, display 138 (see FIG. 1). In one aspect, theTOT_(i) values may be further processed for classification on a relativescale of the various segments of road interspersing the respectivecallouts C₁ to C₂₄. The classified values may then be presented ondisplay 138 as a TOT “heat map” 600, as best seen in FIG. 6. The heatmap 600 presents the calculated TOT_(i) values for the various segmentsof road 402. The TOT_(i) values have been classified into ranges, suchthat sections hashed in the manner of section 602 indicate negativevalues that possibly indicate an unsafe operating condition or,otherwise, an operating condition that possibly causes damage to thehaul truck by operating in a gear that is too high or too low forprevailing environmental conditions. Sections of road hashed in themanner of section 604 indicate a transit time that is significantly morethan the target transit time. Sections of road hashed in the manner ofsection 606 indicate a transit time that is problematically more thanthe target transit time, but less problematically so than the class ofsection hashed in the manner of section 604. Sections of road hashed inthe manner of section 608 are the worst case classification. Unmarked orwhite sections of road such as section 610 represent negligible values,i.e., wherein the measured transit time was about equal to the targettransit time.

One manner of performing this classification is to divide the TOT_(i)values by the distance of the associated section C_(i). The resultingtime per distance values may be segregated for classification, forexample, into quartiles, pentiles, or deciles. As shown in FIG. 6, theheat map 600 presents these classifications with use of hashing, but thepresentation may also be colorized. It will also be appreciated thatlayering may be utilized to enhance the heat map 600 where, for example,the callouts C₁ to C₂₄, the pseudo-lite system, and/or topographicalcontours (not shown) may assist human interpretation of heat map 600.

FIG. 7 provides, by way of example, an optional but preferable use ofTOT_(i) values from an actual operations environment in an open pitmine. Graphical display 700 is a comparative analysis showing curve 702,which represents manufacturer recommended speeds for an empty trucktransiting particular grades of road. Curve 704 is a target speed curveshowing the outcome of what speeds are achievable at grade on aparticular road in an actual open mine according to a panel of expertequipment operators for a certain type of haul truck when the truck isempty. Curve 706 shows actual velocities obtained from a trucktransiting the road. Thus, area 708 between the curves 704, 706represents room for improvement that may be achieved by managerialintervention. In one actual example for a particular open pit mine, themajority of area 708 is primarily on downhill grade. For a fleet ofsimilarly situated trucks in this same mine, just the downhill haulageportion of area 708 represented annually a savings opportunity of 15,600haulage hours.

FIG. 8 provides, by way of example, an optional but preferable use ofTOT_(i) values from an actual operations environment in an open pitmine. Graphical display 800 is a comparative analysis showing curve 802,which represents manufacturer recommended speeds for a fully loadedtruck transiting particular grades of road. Curve 804 is a target speedcurve showing the outcome of what speeds are achievable at grade on aparticular road in an actual open mine according to a panel of expertequipment operators for a certain type of haul truck when the truck isfully loaded. Curve 806 shows actual velocities obtained from a trucktransiting the road. Thus, area 808 between the curves 804, 806represents room for improvement that may be achieved by managerialintervention. In one actual example for a particular open pit mine, themajority of area 808 is primarily on downhill grade. For a fleet ofsimilarly situated trucks in this same mine, just the downhill haulageportion of area 808 represented annually a savings opportunity of 15,200haulage hours.

Returning now to FIG. 3, post-processing 312 provides a variety ofadditional uses for the TOT_(i) values where various aspects of thepost-processing may facilitate closing the opportunity gaps representedby areas 708 and 808. FIG. 9 shows a program architecture 900 foraccomplishing the post-processing 312 according to one embodiment. Inthe architecture 900, computer 136 is constructed and arranged to reportfrom database 140, which contains uploads of operational data fromtrucks 102-112 (see FIG. 1). Computer 136 is provided with a graphicalcomputer interface (GUI) facilitating the program functionalities asrepresented in bar 902. The program functionalities are implemented bysoftware in the programming of computer 136.

In one aspect, a user may interact with a reporting agent 904, which maybe for example a SQL-based reporting agent when the database 140 is arelational database. The reporting agent graphically assists a user inselecting fields from the structure of database 140 for ease ofreporting. Thus, for example, it is possible to combine variables from aplurality of vehicles, average the values, and present the averagevalues in a comparison analysis against similar variables for one ormore vehicles.

A classification agent 906 permits the user to group values into classesof values, such as quartiles or, alternatively, values outside of normalranges that may be of particular importance. For example, as discussedabove, an abnormally low pressure differential across a blower(turbocharger) may diagnose a need for repair in a sick truck havingrelatively high TOT_(i) values when travelling uphill, and thiscondition does not necessarily result in triggering a manufacturer'salarm. A display agent 908 facilitates graphical review of the reporteddata and/or classification results. This may be provided, for example,utilizing a standard graphics package presenting user-selectable optionsfor line graphs, bar charts, and pie charts.

Expert operators may be consulted in providing a library of expert rules910. This library includes user-selectable subroutines establishingcause and effect between TOTi values, operator decisions, truck status(e.g., full or empty), grade, width of road, curvature of road, qualityof road surface, multiple vehicle transit rate, environmentalconditions, and maintenance issues.

A modeling engine 912 may also be provided, for example, to apply thelibrary of expert rules 910 in analyzing other aspects of vehicleperformance. Thus, for example, a sick haul truck may be diagnosed byuse of TOT_(i) values. It is then possible to delimit the field of haultrucks to those having similar TOT_(i) performance issues and to providea model associating the TOT_(i) values with operator events ormechanical events contributing to the TOT_(i) values.

The database 140 may optionally be provided with a maintenance log forthe fleet of vehicles. A maintenance log reporting agent is,accordingly, able to combine vehicle operational data reported throughthe CAN (see FIG. 2) with maintenance or repair events. The data tablesmay be linked, for example, through use of SQL reporting language.

Based upon the interpretation and outcomes of user interaction withprogram functionalities of bar 902, a scheduler 916 is provided toautomate operator training and maintenance events. Thus, a haul truckmay be replaced in its normal usage by another truck, or a sick truckmay be placed on light duty until maintenance facilities are availableto address a diagnosed need for maintenance or repair.

Variants of heat map 600 may include, for example, time progressionseries through a day, month or other interval of time. While heat map600 presents data for a single operator, it is possible to average datafor groups of operators, such as experienced operators versusnon-experienced operators or operators who have received a certain typeof training versus those who have not received the training. Thisaverage data may be retrieved from storage on database 140 and utilizedin step 304 (see FIG. 3) to define a new target speed curve as the basisfor comparison. Outcomes from these types of studies may be utilized tofacilitate operator education.

The program functionalities of bar 902 may be classified to addresscausation types 918, such as roads, trucks, operators and othercausation types associated with problematic TOT_(i) values. Thus, theTOTi values serve as a diagnostic indicator leading to: (1) a diagnosisof causation type, and (2) scheduling for a resolution to resolve thecausation type in an effort to make haulage operations more efficient.Once the diagnosis of causation type is made, the scheduler 916 isavailable to schedule an appropriate resolution event as represented inresolution bar 820. Thus, for example, where the program functionalities902 and analysis of associated processing results indicates that roadsare a causation type associated with high TOT_(i) values, the scheduler916 may be utilized to schedule road maintenance or redesign forpurposes of reducing the opportunity gaps 708, 808 as represented inFIGS. 7 and 8. Thereafter, performance of actual work in the roadmaintenance or redesign makes haulage operations more efficient, asinitiated by the assessment of high TOT_(i) values.

In another example, where the causation type is attributed to the haultruck itself, scheduler 816 may schedule for maintenance or repair ofthe haul truck, or else the haul truck may be reassigned to lighter dutyand replaced in its original role by a stronger truck for purposes ofreducing the opportunity gaps 708, 808 as represented in FIGS. 7 and 8.Similarly, where the causation type is attributed to the operator,scheduler 916 may schedule for training of one or more operators whoshare similar underperformance issues, in order to reduce theopportunity gaps 708, 808. Other causation types may result from a needto reschedule, For example, inclement weather or overwork of theoperators as a group, which may necessitate activity by scheduler 916 inassisting with the reschedule of mine operations.

WORKING EXAMPLES

The following examples teach by way of illustration, and not bylimitation. Accordingly, what is shown below should not be used in anundue manner to inappropriately impose limits on what is claimed.

Example 1: Operator Training Evaluation

Post-processing 312 by use of program functionalities 802 may entailcomparing TOT_(i) values for the same haul truck as it is driven bydifferent operators. Thus, TOT_(i) data for one such operator or thegroup of operators on average may be used to define the target speedcurve in step 304 (see FIG. 3), and utilized for comparison purposesagainst individual operators or other groups of operators. Thus, forexample, TOT_(i) results may be used to measure the effectiveness oftraining administered to one group of operators versus another group ofoperators who have not received the training. The sorting of data mayalso be temporal in the sense that the same comparison may be madecomparing the same operators before and after the training isadministered. In this manner it is possible to ascertain by comparisonwhat are the best driving practices to accelerate mine productionwithout creating unsafe driving conditions and without improperlyoperating the haul trucks. Outcomes from these types of studies may alsobe utilized to facilitate operator education.

Example 2: Operator Training

Operator education may be as simple as providing an individual operatorwith an in-cab display of heat map 600 for his or her personal operationof a vehicle. The operator may, consequently, be able to see areas wherehe or she is underperforming in context of transit time values relativeto other operators, and to take responsive remedial action.

Example 3: Sick Truck

The program functionalities 902 may utilize vehicle analytics todiagnose what may be referred to as a sick truck. In many instances, amanufacturer-provided alarm will indicate a need for service. Even so,many instances exist where the truck is underperforming withouttriggering a manufacturer alarm. Where, for example, a single haul truckconsistently underperforms in a TOT_(i) context on a segment of road nomatter which operator is driving the truck, this may indicate a need formaintenance or repair of the haul truck. By way of example, one cause ofthis type of occurrence may be a faulty blower (turbocharger) where thesituation has not yet reached a state that triggers a vehicle alarm.Other causes may include, for example, a need for engine or transmissionoverhaul. It will be appreciated that expert review of truck operatingdata emanating from the CAN 200 may be utilized to diagnose these typesof operating problems.

Example 4: Safety/Vehicle Abuse Issues

Minimization of the TOT_(i) values may enhance vehicle and operatorsafety in that the haul truck will be traveling at an optimum speed forany particular road segment. But this is only the case where theoperator is not being unsafe by driving too fast or using the wrong gearfor a particular speed. In some instances, TOT_(i) values that are lessthan target time values may indicate a need for managerial interventionin order to promote safety or reduce abuse of equipment. By way ofexample, it may be observed that TOT_(i) values by the same driver overthe same stretch of road diminish in the last hour before shift changeas operators hurry to get home. This should preferably but optionally bedone all the time if the acceleration is not unsafe and does not abusethe equipment. On the other hand, managerial intervention and trainingmay be required if the practices resulting in the acceleration areunsafe or abuse the equipment.

Those of ordinary skill in the art will appreciate that the foregoingdiscussion may be subjected to insubstantial changes without departingfrom the scope and spirit of the invention. Accordingly, the inventorshereby state their intention to rely upon the Doctrine of Equivalents ifneeded to protect the full scope of the invention that is claimed.

1. A method of optimizing a travel speed for a vehicle at a remotelocation, comprising: dividing a road to be traversed by the vehicleinto a plurality of predefined road segments; defining a target speedcurve for the vehicle, the target speed curve correlating a plurality oftarget vehicle speeds for at least one travel condition; determining atarget transit time value for the vehicle in transiting each of theplurality of predefined road segments, wherein said determining a targettransit time value for the vehicle includes utilizing the target speedcurve; ascertaining an actual transit time value for the vehicle as thevehicle travels over at least one of the predefined road segments;comparing the target transit time value and the actual transit timevalue for the at least one predetermined road segment to provide acomparison outcome, wherein the comparison outcome provides a time overtarget value calculated as a difference between the target transit timevalue and the actual transit time value; and adjusting a travel speedfor the vehicle over at least one of the plurality of predefined roadsegments to minimize the time over target value.
 2. The method of claim1, wherein the at least one travel condition comprises one or moreselected from the group consisting of road grade, load state,environmental condition, road condition, and altitude above mean sealevel.
 3. The method of claim 1, wherein said defining a target speedcurve for the vehicle comprises: defining maximum uphill speeds for thevehicle in a loaded condition and an unloaded condition; definingmaximum downhill speeds for the vehicle in the loaded and unloadedconditions; and defining maximum level speeds for the vehicle in theloaded and unloaded conditions.
 4. The method of claim 3, wherein themaximum uphill speeds for the vehicle in the loaded and unloadedconditions vary in a non-linear manner with road grade.
 5. The method ofclaim 3, wherein the maximum downhill speeds for the vehicle in theloaded and unloaded conditions vary in a non-linear manner with roadgrade.
 6. The method of claim 1, wherein the method further comprisesusing the comparison outcome as a threshold indicator to invokepost-processing to identify a causation type, associating the causationtype with a resolution activity to address the causation type,scheduling work to provide the resolution activity, and performing thework as scheduled.
 7. The method of claim 6, wherein the causation typeis selected from the group consisting of roads, trucks, operators andscheduling.
 8. The method of claim 1, further comprising a step ofgenerating a heat map associating the plurality of predetermined roadsegments with the comparison outcome.
 9. The method of claim 8, whereinthe step of generating a heat map includes displaying a plurality ofcallouts along the road, the callouts defining the plurality of roadsegments.
 10. The method of claim 8, wherein the step of generating aheat map includes classifying values associated with the comparisonoutcome, assigning colors to the respective classifications according toa colorized classification scheme, and graphically displaying the roadwith the predetermined road segments according to the colorizedclassification scheme.
 11. The method of claim 1, wherein the targettransit time value and the actual transit time value are intrinsic timevalues.
 12. The method of claim 11, wherein the intrinsic time valuesare selected from the group consisting of velocity and acceleration. 13.The method of claim 1, wherein the remote location includes a wirelesstelemetry system that communicates with a monitoring station, and thevehicle includes a vehicle network, and wherein also the step ofascertaining the actual transit time value includes uploading the actualtransit time value from the vehicle network to the wireless telemetrysystem and thereafter the monitoring station.
 14. The method of claim13, wherein the monitoring station includes a database and a graphicaluser interface that facilitates user-directed reporting from thedatabase, wherein also the step of determining a target transit timevalue includes running a user-directed report.
 15. The method of claim14, wherein the step of running a user-directed report includesreporting from actual transit time values that occur when a particularoperator is operating the vehicle.
 16. The method of claim 14, whereinthe step of running a user-directed report includes reporting fromactual transit time values that occur when a particular operator isoperating a different vehicle.
 17. The method of claim 14, wherein thestep of running a user-directed report includes reporting from actualtransit time values that occur when a plurality of operators areoperating the vehicle.
 18. The method of claim 14, wherein the step ofrunning a user-directed report includes reporting from actual transittime values that occur when a plurality of operators are operating adifferent vehicle.
 19. A system for optimizing a travel speed for anoff-road vehicle, comprising: a telecommunications network, a vehicleincluding a vehicle network having one or more sensors operativelyassociated with the vehicle for providing data selected from the groupconsisting of at least one of time and a speed of the vehicle, atransmitter configured to upload the data to the telecommunicationsnetwork; a processing system operatively associated with thetelecommunications network to operate on the data; the processing systembeing configured with program logic to implement the method of claim 1.20. A non-transitory computer-readable storage medium havingcomputer-executable instructions embodied thereon that, when executed byat least one computer processor cause the processor to perform themethod of claim 1.